Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new feature extraction method in this paper, which uses the boundary semi-labeled samples for solving small sample size problem. The proposed method, which called hybrid feature extraction based on boundary semi-labeled samples (HFE-BSL), uses a hybrid criterion that integrates both the local and global criteria for feature extraction. Thus, it is robust and flexible. The experimental results with three real hyperspectral images show the good efficiency of HFE-BSL compared to some popular and state-of-the-art feature extraction methods

Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weighting (OFW) for supervised feature extraction of hyperspectral data. In the OFW method, the feature vector of each pixel of hyperspectral image is divided to some segments. The weighted mean of adjacent spectral bands in each segment is calculated as an extracted feature. The less the overlap between classes is, the more the class discrimination ability will be. Therefore, the inverse of overlap between classes in each band (feature) is considered as a weight for that band. The superiority of OFW, in terms of classification accuracy and computation time, over other supervised feature extraction methods is established on three real hyperspectral images in the small sample size situation

Ethylene Synthesis and Regulated Expression of Recombinant Protein in Synechocystis sp PCC 6803

The ethylene-forming enzyme (EFE) from Pseudomonas syringae catalyzes the synthesis of ethylene which can be easily detected in the headspace of closed cultures. A synthetic codon-optimized gene encoding N-terminal His-tagged EFE (EFEh) was expressed in Synechocystis sp. PCC 6803 (Synechocystis) and Escherichia coli (E. coli) under the control of diverse promoters in a self-replicating broad host-range plasmid. Ethylene synthesis was stably maintained in both organisms in contrast to earlier work in Synechococcus elongatus PCC 7942. The rate of ethylene accumulation was used as a reporter for protein expression in order to assess promoter strength and inducibility with the different expression systems. Several metal-inducible cyanobacterial promoters did not function in E. coli but were well-regulated in cyanobacteria, albeit at a low level of expression. The E. coli promoter P(trc) resulted in constitutive expression in cyanobacteria regardless of whether IPTG was added or not. In contrast, a Lac promoter variant, P(A1lacO-1), induced EFE-expression in Synechocystis at a level of expression as high as the Trc promoter and allowed a fine level of IPTG-dependent regulation of protein-expression. The regulation was tight at low cell density and became more relaxed in more dense cultures. A synthetic quorum-sensing promoter system was also constructed and shown to function well in E. coli, however, only a very low level of EFE-activity was observed in Synechocystis, independent of cell density

Downsizing a human inflammatory protein to a small molecule with equal potency and functionality

A significant challenge in chemistry is to rationally reproduce the functional potency of a protein in a small molecule, which is cheaper to manufacture, non-immunogenic, and also both stable and bioavailable. Synthetic peptides corresponding to small bioactive protein surfaces do not form stable structures in water and do not exhibit the functional potencies of proteins. Here we describe a novel approach to growing small molecules with protein-like potencies from a functionally important amino acid of a protein. A 77-residue human inflammatory protein (complement C3a) important in innate immunity is rationally transformed to equipotent small molecules, using peptide surrogates that incorporate a turn-inducing heterocycle with correctly positioned hydrogen-bond-accepting atoms. Small molecule agonists (molecular weigh

Auxin Response Factor2 (ARF2) and Its Regulated Homeodomain Gene HB33 Mediate Abscisic Acid Response in Arabidopsis

The phytohormone abscisic acid (ABA) is an important regulator of plant development and response to environmental stresses. In this study, we identified two ABA overly sensitive mutant alleles in a gene encoding Auxin Response Factor2 (ARF2). The expression of ARF2 was induced by ABA treatment. The arf2 mutants showed enhanced ABA sensitivity in seed germination and primary root growth. In contrast, the primary root growth and seed germination of transgenic plants over-expressing ARF2 are less inhibited by ABA than that of the wild type. ARF2 negatively regulates the expression of a homeodomain gene HB33, the expression of which is reduced by ABA. Transgenic plants over-expressing HB33 are more sensitive, while transgenic plants reducing HB33 by RNAi are more resistant to ABA in the seed germination and primary root growth than the wild type. ABA treatment altered auxin distribution in the primary root tips and made the relative, but not absolute, auxin accumulation or auxin signal around quiescent centre cells and their surrounding columella stem cells to other cells stronger in arf2-101 than in the wild type. These results indicate that ARF2 and HB33 are novel regulators in the ABA signal pathway, which has crosstalk with auxin signal pathway in regulating plant growth

Interaction between sugar and abscisic acid signalling during early seedling development in Arabidopsis

Sugars regulate important processes and affect the expression of many genes in plants. Characterization of Arabidopsis (Arabidopsis thaliana) mutants with altered sugar sensitivity revealed the function of abscisic acid (ABA) signalling in sugar responses. However, the exact interaction between sugar signalling and ABA is obscure. Therefore ABA deficient plants with constitutive ABI4 expression (aba2-1/35S::ABI4) were generated. Enhanced ABI4 expression did not rescue the glucose insensitive (gin) phenotype of aba2 seedlings indicating that other ABA regulated factors are essential as well. Interestingly, both glucose and ABA treatment of Arabidopsis seeds trigger a post-germination seedling developmental arrest. The glucose-arrested seedlings had a drought tolerant phenotype and showed glucose-induced expression of ABSCISIC ACID INSENSITIVE3 (ABI3), ABI5 and LATE EMBRYOGENESIS ABUNDANT (LEA) genes reminiscent of ABA signalling during early seedling development. ABI3 is a key regulator of the ABA-induced arrest and it is shown here that ABI3 functions in glucose signalling as well. Multiple abi3 alleles have a glucose insensitive (gin) phenotype comparable to that of other known gin mutants. Importantly, glucose-regulated gene expression is disturbed in the abi3 background. Moreover, abi3 was insensitive to sugars during germination and showed sugar insensitive (sis) and sucrose uncoupled (sun) phenotypes. Mutant analysis further identified the ABA response pathway genes ENHANCED RESPONSE TO ABA1 (ERA1) and ABI2 as intermediates in glucose signalling. Hence, three previously unidentified sugar signalling genes have been identified, showing that ABA and glucose signalling overlap to a larger extend than originally thought

Signal processing and transduction in plant cells: the end of the beginning?

Plants have a very different lifestyle to animals, and one might expect that unique molecules and processes would underpin plant-cell signal transduction. But, with a few notable exceptions, the list is remarkably familiar and could have been constructed from animal studies. Wherein, then, does lifestyle specificity emerge

Spectral-Spatial Hyperspectral Image Classification Based on Homogeneous Minimum Spanning Forest

The combination of spectral and spatial information is known as a suitable way to improve the accuracy of hyperspectral image classification. In this paper, we propose a spectral-spatial hyperspectral image classification approach composed of the following stages. Initially, the support vector machine (SVM) is applied to obtain the initial classification map. Then, we present a new index called the homogeneity order and, using that with K-nearest neighbors, we select some pixels in feature space. The extracted pixels are considered as markers for Minimum Spanning Forest (MSF) construction. The class assignment to the markers is done using the initial classification map results. In the final stage, MSF is applied to these markers, and a spectral-spatial classification map is obtained. Experiments performed on several real hyperspectral images demonstrate that the classification accuracies obtained by the proposed scheme are improved when compared to MSF-based spectral-spatial classification approaches

FUSION OF HYPERSPECTRAL AND PANCHROMATIC IMAGES USING SPECTRAL UNMIXING RESULTS

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spectral unmixing of mixed pixels in hyperspectral images results in spectral signature and abundance fractions of endmembers but gives no information about their location in a mixed pixel. In this paper we have used spectral unmixing results of hyperspectral images and segmentation results of panchromatic image for data fusion. The proposed method has been applied on simulated data using AVRIS Indian Pines datasets. Results show that this method can effectively combine information in hyperspectral and panchromatic images